BNB price analysis

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime

df = pd.read_csv('BNB-USD.csv')
df.head()
    

	Date	Open	High	Low	Close	Adj Close	Volume
0	2017-11-09	2.05314	2.17423	1.89394	1.99077	1.99077	19192200.0
1	2017-11-10	2.00773	2.06947	1.64478	1.79684	1.79684	11155000.0
2	2017-11-11	1.78628	1.91775	1.61429	1.67047	1.67047	8178150.0
3	2017-11-12	1.66889	1.67280	1.46256	1.51969	1.51969	15298700.0
4	2017-11-13	1.52601	1.73502	1.51760	1.68662	1.68662	12238800.0

df.tail()

	Date	Open	High	Low	Close	Adj Close	Volume
2028	2023-05-30	311.810333	314.276611	311.313507	311.684509	311.684509	392038878.0
2029	2023-05-31	311.695709	311.846649	305.376404	306.866699	306.866699	474410245.0
2030	2023-06-01	306.882813	307.613770	303.644562	304.953278	304.953278	354897855.0
2031	2023-06-02	NaN	NaN	NaN	NaN	NaN	NaN
2032	2023-06-03	307.370331	307.370331	307.334808	307.334808	307.334808	380250496.0

df.describe()

	Open	High	Low	Close	Adj Close	Volume
count	2032.000000	2032.000000	2032.000000	2032.000000	2032.000000	2.032000e+03
mean	158.956768	163.860205	153.668686	159.075751	159.075751	9.367168e+08
std	181.151077	186.767035	174.910847	181.076968	181.076968	1.384815e+09
min	1.511360	1.582660	1.462560	1.510360	1.510360	9.284000e+03
25%	14.691567	15.289252	14.206488	14.794200	14.794200	1.561682e+08
50%	28.723639	29.370382	27.973465	28.788359	28.788359	3.960007e+08
75%	307.606750	313.983650	300.660980	307.617782	307.617782	1.289710e+09
max	676.315918	690.931946	634.549500	675.684082	675.684082	1.798295e+10

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2033 entries, 0 to 2032
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       2033 non-null   object 
 1   Open       2032 non-null   float64
 2   High       2032 non-null   float64
 3   Low        2032 non-null   float64
 4   Close      2032 non-null   float64
 5   Adj Close  2032 non-null   float64
 6   Volume     2032 non-null   float64
dtypes: float64(6), object(1)
memory usage: 111.3+ KB

# find null values
df.isnull().sum()

Date         0
Open         1
High         1
Low          1
Close        1
Adj Close    1
Volume       1
dtype: int64

 
    #find duplicate values
df.duplicated().sum()

df = df.dropna()

df['Date']= pd.to_datetime(df['Date'])

df.set_index('Date', inplace=True)

df['diff'] = df['Open'] - df['Close']

plt.figure(figsize=(14,10))
plt.plot(df['Close'])
plt.grid()
plt.xlabel('Year')
plt.ylabel('Close Price')
plt.title('BNB Price')
plt.show()

Daily close

daily_close = df[['Adj Close']]
daily_pct_c = daily_close.pct_change()
daily_pct_c.fillna(0, inplace=True)
print(daily_pct_c)

            Adj Close
Date                 
2017-11-09   0.000000
2017-11-10  -0.097415
2017-11-11  -0.070329
2017-11-12  -0.090262
2017-11-13   0.109845
...               ...
2023-05-29  -0.007837
2023-05-30  -0.000393
2023-05-31  -0.015457
2023-06-01  -0.006235
2023-06-03   0.007809

[2032 rows x 1 columns]

daily_pct_c.hist(bins=50)
plt.show()
print(daily_pct_c.describe())

         Adj Close
count  2032.000000
mean      0.004119
std       0.058566
min      -0.419046
25%      -0.020580
50%       0.001015
75%       0.026164
max       0.697604

pd.plotting.scatter_matrix(daily_pct_c, diagonal='kde', alpha=0.1,figsize=(8,8))
plt.show()

Moving Average

adj_close_px = df['Adj Close']
df['50'] = adj_close_px.rolling(window=50).mean()
df['100'] = adj_close_px.rolling(window=100).mean()

plt.figure(figsize=(16,12))
plt.plot(df['Adj Close'], label='Adj Close')
plt.plot(df['50'], label='50')
plt.plot(df['100'], label='100')
plt.title('Simple Moving Average', fontsize=20)
plt.grid()
plt.legend()

  
    plt.figure(figsize=(16,12))
plt.plot(adj_close_px, label='Adj Close')
plt.plot(adj_close_px.ewm(span=50, adjust=False).mean(), label='50')
plt.plot(adj_close_px.ewm(span=100, adjust=False).mean(), label='100')
plt.title('Exponential Moving Average', fontsize=20)
plt.legend()
plt.grid()
plt.show()
    
  

Volatility Calculation

min_periods = 75
vol = daily_pct_c.rolling(min_periods).std() * np.sqrt(min_periods)
plt.figure(figsize=(10, 8))
plt.plot(vol)
plt.title('Volatility of BNB')
plt.show()

Trading strategy

Moving Average Crossover

short_window = 13
long_window = 49

signals = pd.DataFrame(index=df.index)
signals['signal'] = 0.0

signals['short_mavg'] = df['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
signals['long_mavg'] = df['Close'].rolling(window=long_window, min_periods=1, center=False).mean()
signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:]
                                            > signals['long_mavg'][short_window:], 1.0, 0.0)

signals['positions'] = signals['signal'].diff()

fig = plt.figure(figsize=(14,10))
ax1 = fig.add_subplot(111, ylabel='Price ($)')
df['Close'].plot(ax=ax1, lw=2.)
signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
ax1.plot(signals.loc[signals.positions == 1.0].index,
         signals.short_mavg[signals.positions == 1.0],
         '^', markersize=10, color='red')
ax1.plot(signals.loc[signals.positions == -1.0].index,
         signals.short_mavg[signals.positions == -1.0],
         'v', markersize=10, color='black')
plt.show()

Backtesting

initial_capital= float(100000.0)
positions = pd.DataFrame(index=signals.index).fillna(0.0)
positions['BNB'] = 100*signals['signal']
portfolio = positions.multiply(df['Adj Close'], axis=0)
pos_diff = positions.diff()
portfolio['holdings'] = (positions.multiply(df['Adj Close'], axis=0)).sum(axis=1)
portfolio['cash'] = initial_capital - (pos_diff.multiply(df['Adj Close'], axis=0)).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()

portfolio.head()

Date	cash	total	returns
2017-11-09	100000.0	100000.0	NaN
2017-11-10	100000.0	100000.0	0.0
2017-11-11	100000.0	100000.0	0.0
2017-11-12	100000.0	100000.0	0.0
2017-11-13	100000.0	100000.0	0.0

fig = plt.figure(figsize=(14,10))
ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')
portfolio['total'].plot(ax=ax1, lw=2.)
ax1.plot(portfolio.loc[signals.positions == 1.0].index,
         portfolio.total[signals.positions == 1.0],
         '^', markersize=10, color='m')
ax1.plot(portfolio.loc[signals.positions == -1.0].index,
         portfolio.total[signals.positions == -1.0],
         'v', markersize=10, color='k')
plt.title('Portfolio Value in Time')
plt.show()

The full example is on my Kaggle account. This is the link.

https://www.kaggle.com/code/mixmore/bnb-price-analysis

This is just an explanation of the example on Kaggle.

Deep Side

06/03/2023

BNB price analysis

BNB price analysis

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime

df = pd.read_csv('BNB-USD.csv')
df.head()

df.tail()

df.describe()

df.info()

# find null values
df.isnull().sum()

#find duplicate values
df.duplicated().sum()

df = df.dropna()

df['Date']= pd.to_datetime(df['Date'])

df.set_index('Date', inplace=True)

df['diff'] = df['Open'] - df['Close']

plt.figure(figsize=(14,10))
plt.plot(df['Close'])
plt.grid()
plt.xlabel('Year')
plt.ylabel('Close Price')
plt.title('BNB Price')
plt.show()

Daily close

daily_close = df[['Adj Close']]
daily_pct_c = daily_close.pct_change()
daily_pct_c.fillna(0, inplace=True)
print(daily_pct_c)

daily_pct_c.hist(bins=50)
plt.show()
print(daily_pct_c.describe())

pd.plotting.scatter_matrix(daily_pct_c, diagonal='kde', alpha=0.1,figsize=(8,8))
plt.show()

plt.figure(figsize=(16,12))
plt.plot(adj_close_px, label='Adj Close')
plt.plot(adj_close_px.ewm(span=50, adjust=False).mean(), label='50')
plt.plot(adj_close_px.ewm(span=100, adjust=False).mean(), label='100')
plt.title('Exponential Moving Average', fontsize=20)
plt.legend()
plt.grid()
plt.show()

min_periods = 75
vol = daily_pct_c.rolling(min_periods).std() * np.sqrt(min_periods)
plt.figure(figsize=(10, 8))
plt.plot(vol)
plt.title('Volatility of BNB')
plt.show()

Trading strategy

Moving Average Crossover

06/03/2023

BNB price analysis

BNB price analysis

import numpy as np import pandas as pd import matplotlib.pyplot as pltimport datetime

df = pd.read_csv('BNB-USD.csv')df.head()

df.tail()

df.describe()

df.info()

# find null valuesdf.isnull().sum()

#find duplicate valuesdf.duplicated().sum()

df = df.dropna()

df['Date']= pd.to_datetime(df['Date'])df.set_index('Date', inplace=True)df['diff'] = df['Open'] - df['Close']plt.figure(figsize=(14,10))plt.plot(df['Close'])plt.grid()plt.xlabel('Year')plt.ylabel('Close Price')plt.title('BNB Price')plt.show()

Daily close

daily_close = df[['Adj Close']]daily_pct_c = daily_close.pct_change()daily_pct_c.fillna(0, inplace=True)print(daily_pct_c)

daily_pct_c.hist(bins=50)plt.show()print(daily_pct_c.describe())

pd.plotting.scatter_matrix(daily_pct_c, diagonal='kde', alpha=0.1,figsize=(8,8))plt.show()

plt.figure(figsize=(16,12))plt.plot(adj_close_px, label='Adj Close')plt.plot(adj_close_px.ewm(span=50, adjust=False).mean(), label='50')plt.plot(adj_close_px.ewm(span=100, adjust=False).mean(), label='100')plt.title('Exponential Moving Average', fontsize=20)plt.legend()plt.grid()plt.show()

min_periods = 75 vol = daily_pct_c.rolling(min_periods).std() * np.sqrt(min_periods) plt.figure(figsize=(10, 8))plt.plot(vol)plt.title('Volatility of BNB')plt.show()

Trading strategy

Moving Average Crossover

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime

df = pd.read_csv('BNB-USD.csv')
df.head()

# find null values
df.isnull().sum()

#find duplicate values
df.duplicated().sum()

df['Date']= pd.to_datetime(df['Date'])

df.set_index('Date', inplace=True)

df['diff'] = df['Open'] - df['Close']

plt.figure(figsize=(14,10))
plt.plot(df['Close'])
plt.grid()
plt.xlabel('Year')
plt.ylabel('Close Price')
plt.title('BNB Price')
plt.show()

daily_close = df[['Adj Close']]
daily_pct_c = daily_close.pct_change()
daily_pct_c.fillna(0, inplace=True)
print(daily_pct_c)

daily_pct_c.hist(bins=50)
plt.show()
print(daily_pct_c.describe())

pd.plotting.scatter_matrix(daily_pct_c, diagonal='kde', alpha=0.1,figsize=(8,8))
plt.show()

plt.figure(figsize=(16,12))
plt.plot(adj_close_px, label='Adj Close')
plt.plot(adj_close_px.ewm(span=50, adjust=False).mean(), label='50')
plt.plot(adj_close_px.ewm(span=100, adjust=False).mean(), label='100')
plt.title('Exponential Moving Average', fontsize=20)
plt.legend()
plt.grid()
plt.show()

min_periods = 75
vol = daily_pct_c.rolling(min_periods).std() * np.sqrt(min_periods)
plt.figure(figsize=(10, 8))
plt.plot(vol)
plt.title('Volatility of BNB')
plt.show()